I came across this term when I looked at the vacancies in faculty appointments of the School of Electrical and Electronic Engineering, Nanyang Technological University. In the Wikipedia someone wrote that it is a collection of new techniques in computer science that includes fuzzy logic, neural networks, and probabilistic reasoning. If soft computing is a new term, then conventional computing must
be termed as hard computing. Yes, I get it!

After some search in the Internet, I finally reached a place called BISC (the Berkeley Initiative in Soft Computing). It is from the Electrical Engineering and Computer Science Department, Berkeley University of California.

The principal constituents of soft computing (SC) are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming belief networks, evolutionary computing including DNA computing, chaos theory and parts of learning theory

What is soft computing? Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The basic ideas underlying soft computing in its current incarnation have links to many earlier influences, among them my 1965 paper on fuzzy sets; the 1973 paper on the analysis of complex systems and decision processes; and the 1979 report (1981 paper) on possibility theory and soft data analysis. The inclusion of neural network theory in soft computing came at a later point. At this juncture, the principal constituents of soft computing (SC) are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming belief networks, genetic algorithms, chaos theory and parts of learning theory. What is important to note is that SC is not a melange of FL, NN and PR. Rather, it is a partnership in which each of the partners contributes a distinct methodology for addressing problems in its domain. In this perspective, the principal contributions of FL, NN and PR are complementary rather than competitive.

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